class_docstr: Manages a PyTorch model for training.
class_docstr_tok: ["Manages","a","PyTorch","model","for","training","."]
code: def _start_log_monitor(driver_ip, driver_log_port):
      partition_id = get_partition_id()
      log_path = os.path.join(
          tempfile.gettempdir(), "{}_runner.log".format(partition_id)
      )
      duplicate_stdout_stderr_to_file(log_path)
      logger_thread, thread_stop = LogMonitor.start_log_monitor(
          driver_ip=driver_ip,
          driver_port=driver_log_port,
          log_path=log_path,
          partition_id=partition_id,
      )
      return log_path, logger_thread, thread_stop
  
file_path: BigDL/python/orca/src/bigdl/orca/learn/pytorch/pytorch_pyspark_worker.py
imports: from bigdl.orca.learn.pytorch.torch_runner import TorchRunner
  import torch.distributed
  import logging
  import os
  import tempfile
  import copy
  from pyspark import BarrierTaskContext, TaskContext
  from bigdl.orca.learn.utils import save_pkl, duplicate_stdout_stderr_to_file, get_rank, get_partition_id
  from bigdl.orca.learn.log_monitor import LogMonitor
label_desc: Use image node_id 2 for calling the PytorchPysparkWorker obj's underlying member method code with example usage: obj._start_log_monitor(driver_ip, driver_log_port) and returns: log_path,  logger_thread,  thread_stop